Large Scale Optimization

1. Front Matter

   Pages i-xiv

   PDF

2. Restarting Strategies for the DQA Algorithm

   • Adam J. Berger, John M. Mulvey, Andrzej Ruszczyński

   Pages 1-25

3. Mathematical Equivalence of the Auction Algorithm for Assignment and the ∊-Relaxation (Preflow-Push) Method for Min Cost Flow

   • Dimitri P. Bertsekas

   Pages 26-44

4. Preliminary Computational Experience with Modified Log-Barrier Functions for Large-Scale Nonlinear Programming

   • Marc G. Breitfeld, David F. Shanno

   Pages 45-67

5. A New Stochastic/Perturbation Method for Large-Scale Global Optimization and its Application to Water Cluster Problems

   • Richard H. Byrd, Thomas Derby, Elizabeth Eskow, Klaas P. B. Oldenkamp, Robert B. Schnabel

   Pages 68-81

6. Improving the Decomposition of Partially Separable Functions in the Context of Large-Scale Optimization: a First Approach

   • Andrew R. Conn, Nick Gould, Philippe L. Toint

   Pages 82-94

7. Gradient-Related Constrained Minimization Algorithms in Function Spaces: Convergence Properties and Computational Implications

   • Joseph C. Dunn

   Pages 95-114

8. Some Reformulations and Applications of the Alternating Direction Method of Multipliers

   • Jonathan Eckstein, Masao Fukushima

   Pages 115-134

9. Experience with a Primal Presolve Algorithm

   • Robert Fourer, David M. Gay

   Pages 135-154

10. A Trust Region Method for Constrained Nonsmooth Equations

    • Steven A. Gabriel, Jong-Shi Pang

    Pages 155-181

11. On the Complexity of a Column Generation Algorithm for Convex or Quasiconvex Feasibility Problems

    • Jean-Louis Goffin, Zhi-Quan Luo, Yinyu Ye

    Pages 182-191

12. Identification of the Support of Nonsmoothness

    • C. T. Kelley

    Pages 192-205

13. On Very Large Scale Assignment Problems

    • Yusin Lee, James B. Orlin

    Pages 206-244

14. Numerical Solution of Parabolic State Constrained Control Problems Using SQP- and Interior-Point-Methods

    • Friedemann Leibfritz, Ekkehard W. Sachs

    Pages 245-258

15. A Global Optimization Method For Weber’s Problem With Attraction And Repulsion

    • Costas D. Maranas, Christodoulos A. Floudas

    Pages 259-285

16. Large-Scale Diversity Minimization via Parallel Genetic Algorithms

    • Robert R. Meyer, Jonathan Yackel

    Pages 294-311

17. A Numerical Comparison of Barrier and Modified Barrier Methods For Large-Scale Bound-Constrained Optimization

    • Stephen G. Nash, R. Polyak, Ariela Sofer

    Pages 319-338

18. A Numerical Study of Some Data Association Problems Arising in Multitarget Tracking

    • Aubrey B. Poore, Nenad Rijavec

    Pages 339-361

19. Identifying the Optimal Face of a Network Linear Program with a Globally Convergent Interior Point Method

    • Mauricio G. C. Resende, Takashi Tsuchiya, Geraldo Veiga

    Pages 362-387

20. Solution of Large Scale Stochastic Programs with Stochastic Decomposition Algorithms

    • Suvrajeet Sen, Jason Mai, Julia L. Higle

    Pages 388-410

On February 15-17, 1993, a conference on Large Scale Optimization, hosted by the Center for Applied Optimization, was held at the University of Florida. The con­ ference was supported by the National Science Foundation, the U. S. Army Research Office, and the University of Florida, with endorsements from SIAM, MPS, ORSA and IMACS. Forty one invited speakers presented papers on mathematical program­ ming and optimal control topics with an emphasis on algorithm development, real world applications and numerical results. Participants from Canada, Japan, Sweden, The Netherlands, Germany, Belgium, Greece, and Denmark gave the meeting an important international component. At­ tendees also included representatives from IBM, American Airlines, US Air, United Parcel Serice, AT & T Bell Labs, Thinking Machines, Army High Performance Com­ puting Research Center, and Argonne National Laboratory. In addition, the NSF sponsored attendance of thirteen graduate students from universities in the United States and abroad. Accurate modeling of scientific problems often leads to the formulation of large­ scale optimization problems involving thousands of continuous and/or discrete vari­ ables. Large scale optimization has seen a dramatic increase in activities in the past decade. This has been a natural consequence of new algorithmic developments and of the increased power of computers. For example, decomposition ideas proposed by G. Dantzig and P. Wolfe in the 1960's, are now implement able in distributed process­ ing systems, and today many optimization codes have been implemented on parallel machines.

• algorithms
• genetic algorithms
• linear optimization
• Mathematica
• mechanics
• modeling
• nonlinear optimization
• optimal control
• optimization
• perturbation
• programming
• structural mechanics

• Center for Applied Optimization, University of Florida, Gainesville, USA

  W. W. Hager, D. W. Hearn, P. M. Pardalos

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